Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Abstract

Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing

“…In their article about data science projects on industrial data in brownfields [11], the authors point out the importance of thorough planning of data acquisition. They also emphasize that findings from greenfield approaches need to be checked for generalizability before applying them to brownfield approaches, and vice versa.…”

Context-Based Resilience in Cyber-Physical Production System

“…However, resilience also requires techniques and strategies to perform recovery actions and ensure continuity in the system operability also in case of disruptions. A recent ever-growing interest has been devoted to resilience challenges [2,14,15], often related to the notion of self-adaptation, as witnessed by an increasing number of surveys on this topic [16][17][18][19]. Targets of recent approaches addressing CPS resilience vary from cyber-security [20][21][22][23], to cyberphysical power systems [24,25] and to cyber-physical production systems [26][27][28][29][30].…”

“…Furthermore, before including each service of S among the relevant ones, the candidate service is checked to verify the following conditions (line 8-18): (a) if the service type is "re-configuration" (type function) and the value of its output parameters does not exceed any parameter bound (outputwithinbounds function), then the candidate service is selected as relevant (lines 9-10); (b) if the service type is "substitution" (line 11), the alternative smart machines associated with the service are identified (line 12) and among them those machines that are currently unavailable because they are in warning or error status (lines 5-6) are excluded (line 13); the candidate service is selected as relevant if the resulting set of smart machines suitable for the substitution is not empty (lines [14][15][16].…”